肺癌作为全球恶性肿瘤中发病率和死亡率较高的一种，手术是其主要治疗手段。然而，肺切除手术常引起肺功能下降，影响患者术后功能恢复及生活质量。准确预测术后肺功能对制定个体化手术方案、降低并发症风险及改善患者预后具有重要意义。目前，研究者已开发出多种结合临床指标、影像学参数及生物标志物的预测模型，用于评估肺癌患者术后肺功能变化。然而，现有模型在准确性、特异性、标准化及临床推广方面仍存在诸多挑战。本文系统综述肺癌术后肺功能的影响因素，并对现有预测模型进行总结与评价，以期为患者术前评估、手术方案选择及术后管理提供参考，进而为改善治疗效果与患者生活质量提供理论依据。

       Lung cancer is a kind of malignant tumor with high morbidity and mortality in the world，and surgery is the main treatment．However，pneumonectomy is often accompanied by the loss of lung function，which seriously affects the recovery of lung function and the  quality of life．Accurate prediction of postoperative pulmonary function is of great significance for formulating individualized surgical plans，reducing the risk of complications and improving the prognosis of patients．At present，researchers have developed a variety of predictive models that combine clinical indicators，imaging parameters，and biomarkers to evaluate postoperative lung function changes in patients with lung cancer．However，there are still many challenges in the accuracy，specificity，standardization and clinical promotion of existing models．This paper systematically reviews the influencing factors of lung function after lung cancer surgery，and summarizes and evaluates the existing prediction models，in order to provide a reference for preoperative evaluation，surgical plan selection and postoperative management of patients，and then provide theoretical basis for improving treatment effect and quality of life of patients．

目的 通过建立急性心力衰竭（AHF）患者服药依从性预测模型,提高AHF患者的服药依从性和临床管理效果。方法 纳入2021年1月—2023年12月在广州市番禺区何贤纪念医院住院治疗的580例AHF患者,通过收集患者的一般人口学资料、疾病相关资料及出院后6个月的服药依从性数据,应用Logistic回归模型分析患者服药依从性的影响因素,并基于影响因素建立预测模型。结果 患者服药依从性总体良好（75%）。依从性良好组与依从性差组的年龄、独居情况、合并基础病、服药种类、疾病了解评分、治疗信心评分和自我控制信心评分比较差异有统计学意义（P<0.05）。Logistic 回归分析显示危险因素包括年龄≥60岁（OR=1.774）、独居（OR=1.871）、合并基础病≥2种（OR=1.719）和服药种类≥7种（OR=1.456）。而疾病了解评分（OR=0.923）、治疗信心评分（OR=0.946）和自我控制信心评分（OR=0.901）是保护因素（P<0.05）。基于上述因素建立的预测模型,通过ROC曲线验证,曲线下面积为0.815（95%CI：0.780~0.850）,提示所构建的模型具有良好的区分度。对该模型的校准度进行评价,P=0.528,提示该预测模型拟合度良好。此外,该预测模型的一致性指数为0.738,说明模型的预测性能良好。绘制的决策曲线中,曲线位于极端线之上,当阈概率取值在9%~59%时,对应的净获益率为0~27%,提示建立的模型具有优秀的临床有效性。结论 AHF患者的服药依从性受到多种因素的影响,包括年龄、居住状态、合并基础病种类及服药种类等。

Objective To establish a predictive model for medication compliance among acute heart failure（AHF）patients in order to enhance their therapeutic compliance and optimize clinical outcomes. Methods A total of 580 AHF inpatients at He Xian Memorial Hospital in Panyu District, Guangzhou between January 2021 and December 2023 were enrolled. Demographic information, disease-specific data,as well as post-discharge medication compliance records within six-month were collected by investigators. Utilizing logistic regression analysis revealed several influential determinants affecting medication compliance which formed the basis for constructing our predictive model. Results Generally,patient compliance was good（75%）. The comparison between the good compliance group and the poor compliance group showed that there were significant differences in age, living alone,combined with underlying diseases, types of medication, disease understanding score, treatment confidence score and self-control confidence score（P<0. 05）. Logistic regression analysis showed that independent risk indicators including individuals aged ≥60 years（odds ratio[OR]=1. 774）, those living alone（OR=1. 871）, presence of two or more underlying diseases（OR=1. 719）, along with consumption of seven or more medications daily（OR=1. 456）. Conversely,disease awareness score（OR=0. 923）, treatment confidence score（OR=0. 946）, and self-control confidence score（OR=0. 901）were identified as independent protective factors. Validation using receiver operating characteristic curves demonstrated robust predictive performance with an area under curve value of 0. 815（95%CI：0. 780-0. 850）, affirming its efficacy. The calibration of the model was evaluated, with a P-value of 0. 528, indicating good fit of the predictive model. Additionally, the concordance index（C-index）of the model was 0. 738, suggesting its excellent predictive performance. The decision curve analysis revealed that the curve was above the extreme lines, with a net benefit rate ranging from 0 to 27% when the threshold probability falls between. Conclusions The medication compliance of AHF patients is influenced by various factors, including age, living arrangement, the number of underlying diseases, and the number of medications taken. Targeted interventions such as enhancing patient education, simplifying treatment regimens, and improving social support can effectively improve the medication compliance of AHF patients. The predictive model established in this study provides a scientific basis for clinicians to develop more precise and effective individualized intervention measures,thereby improving the prognosis and quality of life.

       目的   构建并验证主动脉夹层B型（TBAD）患者急性期预后的列线图预测模型，帮助临床医生在急性期内更准确地评估TBAD患者的死亡风险，并制定更合适的治疗策略。方法   回顾性分析从重症监护医学信息数据库v2.2 中提取的399例 TBAD患者的人口学资料和临床资料，结局为TBAD患者急性期（≤14 d）内死亡。先采用最小绝对收缩选择算法回归筛选特征变量，再采用多因素分析确定独立预后因素，并据此构建预测模型。通过受试者工作特征曲线、校准曲线、决策曲线分析（DCA）评价列线图预测模型的性能和临床适用性。结果  APS Ⅲ评分、二氧化碳总量、红细胞分布宽度为TBAD患者14 d内死亡的独立预测因素。列线图预测模型在内部验证中的受试者工作特征曲线下面积为0.776（95% CI：0.691 ~ 0.860），Hosmer-Lemeshow 检验P=0.604，校准曲线和标准曲线高度重合，表明该模型具有良好的区分度和校准度。同时，DCA曲线显示，预测模型在大部分的阈值概率范围内提供了显著的净收益。结论   本研究基于APS Ⅲ评分、二氧化碳总量、红细胞分布宽度构建的列线图预测模型可以较准确地预测TBAD患者14 d内的死亡风险，有助于临床医生制定更合适的个体化治疗策略。

       Objective  To develop and verify a nomogram for predicting acute phase outcomes in patients with type B aortic dissection（TBAD），enabling clinicians to more precisely evaluate mortality  risk in TBAD patients during the acute stage and to devise better treatment plans．Methods  This retrospective study analyzed demographic and clinical data of 399 TBAD patients from the Medical Information Mart for Intensive Care IV v2.2，focusing on mortality within 14 days of the acute phase in TBAD patients．Initially，the Least Absolute Shrinkage and Selection Operator regression was employed for feature variable selection，and then multivariate analysis was used to identify independent prognostic factors for constructing the predictive model．The nomogram predictive model’s effectiveness and clinical applicability were assessed via the Receiver Operating Characteristic curve，calibration curve，and Decision Curve Analysis（DCA）．Results  Acute Physidogy Score Ⅲ score，total carbon dioxide，and red blood cell distribution width emerged as independent predictors of 14-day mortality in TBAD patients．The internal validation of the nomogram predictive model showed an area under the curve of 0.776（95%CI：0.691-0.860），with a Hosmer-Lemeshow test P-value of 0.604．The close alignment of the calibration and standard curves suggested the model’s strong discriminative power and calibration．Furthermore，the DCA curve  revealed that the predictive model offered substantial net benefits within a wide  range of threshold probabilities．Conclusions  This study's nomogram，developed using APS Ⅲ score，total carbon dioxide，and  red blood cell distribution width，accurately predicts the 14-day mortality risk in TBAD patients，assisting clinicians in creating better personalized treatment plans．